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deberta-xxlarge-v2-mnli

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@@ -11,34 +11,29 @@ Please check the [official repository](https://github.com/microsoft/DeBERTa) for
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  This the DeBERTa V2 xxlarge model fine-tuned with MNLI task, 48 layers, 1536 hidden size. Total parameters 1.5B.
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- #### Fine-tuning on NLU tasks
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  We present the dev results on SQuAD 1.1/2.0 and several GLUE benchmark tasks.
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- | Model | SQuAD 1.1 | SQuAD 2.0 | MNLI-m/mm | SST-2 | QNLI | CoLA | RTE | MRPC(acc/f1) | QQP |STS-B|
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- |---------------------------|-----------|-----------|-------------|-------|------|------|--------|--------------|------|-----|
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- | BERT-Large | 90.9/84.1 | 81.8/79.0 | 86.6/- | 93.2 | 92.3 | 60.6 | 70.4 | 88.0/- | 91.3 |90.0 |
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- | RoBERTa-Large | 94.6/88.9 | 89.4/86.5 | 90.2/- | 96.4 | 93.9 | 68.0 | 86.6 | 90.9/- | 92.2 |92.4 |
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- | XLNet-Large | 95.1/89.7 | 90.6/87.9 | 90.8/- | 97.0 | 94.9 | 69.0 | 85.9 | 90.8/- | 92.3 |92.5 |
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- | [DeBERTa-Large](https://huggingface.co/microsoft/deberta-large) | 95.5/90.1 | 90.7/88.0 | 91.3/91.1 | 96.5 | 95.3 | 69.5 | 86.6 | 92.6/94.6 | 92.3 |92.5 |
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- | [DeBERTa-XLarge](https://huggingface.co/microsoft/deberta-xlarge) | -/- | -/- | 91.5/91.2 | - | - | - | 89.5 | 92.1/94.3 | - |- |
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- | [DeBERTa-XLarge-V2](https://huggingface.co/microsoft/deberta-xlarge-v2) | - | - | 91.7/91.6 | - | - | - | - | - | - |- |
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- |**[DeBERTa-XXLarge-V2](https://huggingface.co/microsoft/deberta-xxlarge-v2)**|**96.1/91.4**|**92.2/89.7**|**91.7/91.9**| - | - | - | - | - | - |- |
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- |**[DeBERTa-XLarge-V2-MNLI](https://huggingface.co/microsoft/deberta-xlarge-v2-mnli)**| - | - | 91.7/91.6 | - | - | - | 93.9 | - | - |- |
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- |**[DeBERTa-XXLarge-V2-MNLI](https://huggingface.co/microsoft/deberta-xxlarge-v2-mnli)**| - | - |**91.7/91.9**| - | - | - | 93.5 | - | - |- |
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- ## Note
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-
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- To try the **XXLarge** model with **[HF transformers](https://huggingface.co/transformers/main_classes/trainer.html)**, you need to specify **--sharded_ddp**
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- ```bash
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  cd transformers/examples/text-classification/
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  export TASK_NAME=mrpc
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- python -m torch.distributed.launch --nproc_per_node=8 run_glue.py --model_name_or_path microsoft/deberta-xxlarge-v2-mnli \
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  --task_name $TASK_NAME --do_train --do_eval --max_seq_length 128 --per_device_train_batch_size 4 \
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  --learning_rate 3e-6 --num_train_epochs 3 --output_dir /tmp/$TASK_NAME/ --overwrite_output_dir --sharded_ddp --fp16
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  ```
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  This the DeBERTa V2 xxlarge model fine-tuned with MNLI task, 48 layers, 1536 hidden size. Total parameters 1.5B.
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+ ### Fine-tuning on NLU tasks
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  We present the dev results on SQuAD 1.1/2.0 and several GLUE benchmark tasks.
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+ | Model | SQuAD 1.1 | SQuAD 2.0 | MNLI-m/mm | SST-2 | QNLI | CoLA | RTE | MRPC | QQP |STS-B |
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+ |---------------------------|-----------|-----------|-------------|-------|------|------|--------|-------|-------|------|
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+ | | F1/EM | F1/EM | Acc | Acc | Acc | MCC | Acc |Acc/F1 |Acc/F1 |P/S |
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+ | BERT-Large | 90.9/84.1 | 81.8/79.0 | 86.6/- | 93.2 | 92.3 | 60.6 | 70.4 | 88.0/- | 91.3/- |90.0/- |
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+ | RoBERTa-Large | 94.6/88.9 | 89.4/86.5 | 90.2/- | 96.4 | 93.9 | 68.0 | 86.6 | 90.9/- | 92.2/- |92.4/- |
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+ | XLNet-Large | 95.1/89.7 | 90.6/87.9 | 90.8/- | 97.0 | 94.9 | 69.0 | 85.9 | 90.8/- | 92.3/- |92.5/- |
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+ | [DeBERTa-Large](https://huggingface.co/microsoft/deberta-large)<sup>1</sup> | 95.5/90.1 | 90.7/88.0 | 91.3/91.1| 96.5|95.3| 69.5| 91.0| 92.6/94.6| 92.3/- |92.8/92.5 |
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+ | [DeBERTa-XLarge](https://huggingface.co/microsoft/deberta-xlarge)<sup>1</sup> | -/- | -/- | 91.5/91.2| 97.0 | - | - | 93.1 | 92.1/94.3 | - |92.9/92.7|
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+ | [DeBERTa-XLarge-V2](https://huggingface.co/microsoft/deberta-xlarge-v2)<sup>1</sup>|95.8/90.8| 91.4/88.9|91.7/91.6| **97.5**| 95.8|71.1|**93.9**|92.0/94.2|92.3/89.8|92.9/92.9|
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+ |**[DeBERTa-XXLarge-V2](https://huggingface.co/microsoft/deberta-xxlarge-v2)<sup>1,2</sup>**|**96.1/91.4**|**92.2/89.7**|**91.7/91.9**|97.2|**96.0**|**72.0**| 93.5| **93.1/94.9**|**92.7/90.3** |**93.2/93.1** |
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+ --------
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+ #### Notes.
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+ - <sup>1</sup> Following RoBERTa, for RTE, MRPC, STS-B, we fine-tune the tasks based on [DeBERTa-Large-MNLI](https://huggingface.co/microsoft/deberta-large-mnli), [DeBERTa-XLarge-MNLI](https://huggingface.co/microsoft/deberta-xlarge-mnli), [DeBERTa-XLarge-V2-MNLI](https://huggingface.co/microsoft/deberta-xlarge-v2-mnli), [DeBERTa-XXLarge-V2-MNLI](https://huggingface.co/microsoft/deberta-xxlarge-v2-mnli). The results of SST-2/QQP/QNLI/SQuADv2 will also be slightly improved when start from MNLI fine-tuned models, however, we only report the numbers fine-tuned from pretrained base models for those 4 tasks.
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+ - <sup>2</sup> To try the **XXLarge** model with **[HF transformers](https://huggingface.co/transformers/main_classes/trainer.html)**, you need to specify **--sharded_ddp**
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+
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+ ```bash
 
 
 
 
 
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  cd transformers/examples/text-classification/
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  export TASK_NAME=mrpc
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+ python -m torch.distributed.launch --nproc_per_node=8 run_glue.py --model_name_or_path microsoft/deberta-xxlarge-v2 \
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  --task_name $TASK_NAME --do_train --do_eval --max_seq_length 128 --per_device_train_batch_size 4 \
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  --learning_rate 3e-6 --num_train_epochs 3 --output_dir /tmp/$TASK_NAME/ --overwrite_output_dir --sharded_ddp --fp16
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  ```